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CHAPTER 3
RESEARCH METHODOLOGY
3.1 INTRODUCTION
Chapter two focused on issues that cause variation in loyalty perception of a user for
different websites. The main conclusions derived from previous chapter are: (1) relative
importance of e-loyalty antecedents varies with varying website category, (2) less emphasis
has been given to determine the relative importance across different websites in existing
studies, (3) practical strategies used by the websites to ensure e-loyalty is absent in literature,
(4) website can be categorized on the basis of user’s primary need. The purpose of this
chapter is to discuss the research methodology adopted to conduct this study to address our
research issues.
Research methodology can be understood as the science and philosophy behind the
systematic solution to a research problem (Adams et al., 2007). Jonker and Pennink (2010)
considered methodology as ‘action repertoire’ which includes preparing a repertoire based on
theoretical and practical foundations, according to which the researcher structures the logic of
research to address the research problems. It includes the study of various steps that are
usually implemented in a research for studying the research problem along with the
supportive logic (Kothari, 2004). The following sections report the research
methods/techniques adopted in this study.
3.2 RESEARCH PHILOSOPHY
Research is not ‘neutral’ but incorporates focus, aims, ambitions, devotion, values,
observation, assumptions and abilities of the researcher. Hence, it is imperative to look upon
the philosophy used in this study. Research paradigm, described by (Blaikie, 2000;
Bhattacherjee, 2012) or research philosophy, explained by (Saunders, Lewis and Thornhill,
2009) relates to the development of knowledge and the nature of that knowledge. Easterby-
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Smith, Thorpe and Lowe (2008) identified three reasons to clarify the importance of research
philosophy:
1. Help researcher to determine research methodology and overall research strategy.
2. Avoid unnecessary work and inappropriate use of different methods and methodologies
by recognizing the limitation of particular approaches.
3. Assist researcher to be innovative in selection of methods that were previously outside
his/her experience.
Two major ways of thinking about research philosophy are ontology (what is the nature
of reality) and epistemology (what can be known) which encompasses various philosophies.
Ontology concerns with the nature of reality i.e. what constitutes reality and how can we
understand existence (Saunders, Lewis and Thornhill, 2009). The belief is that reality subsists
regardless of human observers. Epistemology concerns what comprises acceptable
knowledge in the field of study i.e. what we know and how we know it (Porta and Keating,
2008). The belief can be justified using logical reasoning and experimentation. Justification
and nature of facts (nature of data and method of acquisition) are central themes. According
to Reber (1995) epistemology and ontology are within the foundation realm of philosophy
and mutually support one another while Cohen, Marion and Morrison (2007) and Hitchcock
and Hughes (1995) stated that ontological assumptions provide base to epistemological
assumptions, which leads to methodological consideration and eventually steers to
instrumentation and data collection.
Three main types of philosophies discussed in the literature are positivism, realism and
interpretivism. A positivist philosophy assumes that reality is fixed, directly measurable, and
there exists one truth, one external reality. Positivism believes that there is an objective
reality independent of human behaviour that is not a creation of the human mind (Morgan
and Smircich, 1980). Positivism is typically associated with quantitative research methods
such as experiments and surveys and emphasizes measuring and counting (Remenyi et al.,
1998). Positivists start with a theory and generate hypotheses that are subjected to empirical
examination.
Most research in social science applied an objective approach (epistemology) and used
the standardized data collection tool like surveys (Bhattacherjee, 2012). They begin with
clear sets of positivist assumptions, including hypotheses, and then proceed to test the
hypotheses (Saunders, Lewis and Thornhill, 2009). This research is also not an exception and
is positive in approach. It aims to develop hypotheses and further tests it by data collection.
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3.3 RESEARCH APPROACH
An individual can proceed deductively or inductively to identify causal relationships
that account for a particular phenomenon. Deductive approach develops a theory and
hypothesis (or hypotheses), and design research strategy to test the hypothesis while in
inductive approach, data is collected and theory is developed as a result of data analysis
(Porta and Keating, 2008).Remenyi et al. (1998) recommended deductive approach in
research in which a theory or hypothesis is built and research strategy is developed to test the
hypothesis, mostly in disciplines where agreed facts and established theories are available.
The theories and facts can be derived by analyzing the existing literature on a particular
phenomenon or subject. The researcher first collects the available literature of interest and
then synthesizes it to formulate framework and theories. Sekaran (2003) described this
approach as a hypothetico-deductive method. It starts with the theoretical framework;
hypotheses are formulated and end with a logical deduction from the results.
Deductive and inductive approach is generally followed with a quantitative and
qualitative research (Newman, 2003). Thus, research can also be dichotomized as qualitative
and quantitative (Powell, 2004; Denscombe, 2007). The qualitative research emphasizes on
processes and meanings that are not measured in terms of quantity amount, intensity or
frequency, while quantitative research methods are used within natural science, the meanings
are often derived from numbers and the aim is usually explanatory, to permit generalizations
and to enable predictions (Saunders, Lewis and Thornhill, 2009: Hair et al., 2010). Deductive
research is associated with quantitative research while inductive research is related to
qualitative research (Bryman and Bell, 2005). “Positivist methods, such as laboratory
experiments and survey research, are aimed at theory (or hypotheses) testing … … employ a
deductive approach to research, starting with a theory and testing theoretical postulates using
empirical data” (Bhattacherjee, p. 35).
In the present study, a conceptual framework and associated hypotheses are developed.
Altogether 13 hypotheses were developed on the relationship between dependent and
independent variable. The variables are e-loyalty, e-satisfaction, e-service quality, e-
perceived value, e-trust, number of members and number of peers. The data on these
constructs was collected by participants through a survey. The hypotheses were tested against
the collected data. This study’s approach is deductive, and accumulated data is quantitative
hence study is quantitative rather than qualitative.
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3.4 RESEARCH PURPOSE
Exploratory, descriptive, and explanatory are the three classifications of research
available when dealing with a research problem (Yin, 1994). An exploratory study is a means
to look out what is happening, to search for new insight, to interrogate and to assess
phenomena in a new light (Robson, 2002). Descriptive research is limited to frequency
distributions and may be qualitative or quantitative. It is normally restricted to summary
statistics such as mean (Sue and Ritter, 2012). Explanatory research establishes causal
relationships between variables, emphasizes on studying a situation or a problem to explain
the associations between variables (Saunders, Lewis and Thornhill, 2009). Sue and Ritter
(2012, p. 2) explained, “explanatory studies are characterized by research hypotheses that
specify the nature and direction of the relationships between or among variables being
studied … the data are quantitative and almost always require the use of a statistical test to
establish the validity of the relationships”. Theories or at least hypotheses are responsible
forces that affect a certain phenomenon to occur in an explanatory research (Cooper and
Schindler, 2008). This study aims to test causal relationships between e-loyalty and its
antecedents and intends to collect data for hypothesis testing. It also aims to apply rigorous
statistical tests to ascertain the reliability and validity of relationships that underlie conceptual
framework. Thus, this study is explanatory in nature.
3.5 RESEARCH STRATEGY
Yin (1994) defined five primary research strategies: experiments, surveys, archival
analysis, histories, and case studies. No research strategy is inherently inferior or superior to
any other and choice of research strategy will be guided by research question(s) and
objectives, the extent of existing knowledge, the amount of time and other available
resources, as well as philosophical underpinnings (Saunders, Lewis and Thornhill, 2009).
Aaker, Kumar and Day (2004) described that adopting survey method for research depends
on number of factors – type of population, sampling, question content, question format and
costs. The purpose of current research is to gain a better understanding of what determines
loyalty for a website and how the same user perceives different antecedents of e-loyalty for
various websites. Survey is best option to answer ‘what’ and ‘how’ questions (Yin, 1994). It
gives the opportunity to collect quantitative data that can be analyzed using inferential
statistics. Also to arrive at valid results, collection of data from substantial number of
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participants is required. Survey through questionnaires is cost-effective in such cases. Thus,
data collection through survey seems the most suitable plan for this study. For
implementation, a self-administered survey is used. The advantages are cost effectiveness and
accuracy (Aaker, Kumar and Day, 2004), privacy (Burns and Bush, 1992) and easy to
understand for respondents (Grossnickle and Raskin, 2001).
Bryman and Bell (2005) defined two types of survey – questionnaire and structured
interview. Structured interview demands the presence of the interviewer while a respondent
can fill questionnaires on its own. The choice of a survey method is based on different factors
which include type of population, sampling, response rate, costs, question format, subject
content and duration of data collection (Aaker, Kumar and Day, 2004). The proposed
conceptual model contained a number of research hypotheses that required an empirical
examination to conclude from the study. It demanded quantitative data collection and the
validity of results depend heavily on the number of responses. Thus, it is essential to reach
sufficient number of respondents. The questionnaireprovides a quantitative method of data
collection - the responses, data or information needed in numerical terms. Therefore,
questionnaire was used as a survey item for data gathering.
3.6 INSTRUMENT DEVELOPMENT
Denoting the critical significance of instrument in generating accurate survey
assessments, Straub, Gefen and Boudreau (2005) emphasized the use of previously validated
available instruments. The researchers should implement already authenticated measurement
items rather than developing a new one for efficiency reasons. Thus, the instrument items in
this study were adopted from existing literature, but with some adaptations in the context of
the present study. Altogether tenconstructs were to be measured: convenience, contact
interactivity, customization, responsiveness, e-trust, e-perceived value, e-satisfaction, number
of members, number of peers and e-loyalty. Appendix A lists the original construct items
with their corresponding literature sources.Finally, 33 items was derived to measure these ten
constructs. The measurement items and their corresponding sources are listed in table 3.1.
The scale of convenience, contact interactivity and convenience was adopted from
Srinivasan, Anderson and Ponnavolu (2002). Responsiveness items were adopted from
Semeijn et al. (2005). E-perceived value was adopted from Luarn and Lin (2003) and e-trust
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from Cyr et al. (2007). E-satisfaction items were adopted from Sheng and Liu (2010).Number
of members and number of peers items were adopted from Lin and Lu (2011).
Table 3.1: Measurement items
Construct Measurement items Adopted from
Number of members 1. I think a good number of people use this website. 2. I think most people are using this website. 3. I think there will still be many people using this website.
Lin and Lu (2011)
Number of peers
1. I think many friends around me use this website. 2. I think most of my friends are using this website. 3. I anticipate many friends will use this website in the
future.
Lin and Lu (2011)
Convenience
1. A first-time user can locate the items on this website easily.
2. This website does not take much time to meet my demands.
3. This website is a user-friendly site. 4. This website is very convenient to use.
Srinivasan, Anderson and Ponnavolu (2002)
Contact interactivity
1. I feel this is a very engaging website. 2. This is a very dynamic website. 3. My interaction with this website is clear and
understandable.
Srinivasan, Anderson and Ponnavolu (2002)
Customization
1. This website makes recommendations that match my needs.
2. The advertisements and promotions that this website sends to meare tailored to my situation.
3. This website makes me feel that I am a unique customer. 4. I believe that this websiteis customized to my needs.
Srinivasan, Anderson and Ponnavolu (2002)
Responsiveness 1. It is easy to get in touch with the website. 2. Website is always interested in feedback. 3. Website quickly responds to user request.
Semeijn et al. (2005)
E-perceivedvalue
1. I get much more than the worth of my time, effort and money.
2. Based on simultaneous considerations of what I give and what I receive, I consider this website to be valuable.
3. The choices of products and/or services offered by the website are better than its competitor.
4. After every visit, it makes me feel, it is worth using this website.
Luarn and Lin (2003)
E-trust
1. I can trust this website. 2. I trust the information presented on this website. 3. I feel this website will keep my data secure and will not
share with anyone else.
Cyr et al. (2007)
E-satisfaction
1. I feel satisfied with all myexperiences on this site. 2. I feel wise to use this site. 3. Generally speaking, I think it is an accurate decision to go
on to this particular website for my needs and requirements.
Sheng and Liu (2010)
E-loyalty 1. I prefer this website. 2. I will use the same website again. 3. Iwill recommend this website to others.
Semeijn et al. (2005)
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All the scales that have been used to measure the research construct were adapted from
well-cited journal’s article and showed high consistency (Cronbach’s alpha) in respective
studies. 5-point Likert scale was used for measurement. The scale was given to indicate the
response where each scale item has five response categories, ranging from “strongly
disagree” to “strongly agree".
3.7 QUESTIONNAIRE DEVELOPMENT
Two questionnaireswere used to accomplish the objectives of this study. Questionnaire
(A) was used to test the research hypotheses, and questionnaire (B) purpose was to ensure
consistency and to validate the obtained the results. The difference between these two
questionnaires is that questionnaire (A) demanded responses about research constructs for
any preferred website in three categories of the website. The categories are service website,
product website and social networking website. However, the questionnaire (B) is targeted
for three specificwebsites in each category. The websites are Amazon.in (product website),
Google.com (service website) and Facebook.com (social networking website).
3.7.1 Questionnaire (A)
Questionnaire A was developed to measure online web user perceptions about the
research constructs – online users are asked to indicate responses for their preferred website
in each of the three categories of the website, i.e. product website, service website and social
networking website.The final version of the questionnaire is listed in Appendix B.
The questionnaire is divided into two parts – first part contains general demographic
information, which includes gender, education, age and occupation while the second part
contains questions about e-loyalty and its antecedents. Contact details are also requested
which helped to identify other probable participants. Second part of the questionnaire
contains measurement items about e-loyalty behaviour.
Same users with at least five visits per month for a service website and a social
networking website and at least two visits per month to a product website were considered in
this study. This is done in accordance with following observations
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1. According to a report by International Market Research Bureau (IMRB) and I-Cube
(2014, cited in Prabhudesai, 2014), 97% of the ‘active’ Internet users used the Internet at
least 2 times and almost 85% went online at least 4 times a month.
2. As per ComScore (2013, cited in Mitra, 2014), an internet analytics company, the major
drivers of web behaviour are social networking and services. Further, they share, India’s
Internet population spent 23% of time on online services, 25% on social networking while
retail accounted only 3% of the total time spent.
In line with these statistics, an attempt to cater all typeof active Internet users was done
and the frequency for service website and social website was kept at least five times a month,
for product website the frequency is low, at least two times per month.
3.7.2 Questionnaire (B)
Questionnaire (B) is a slight modification of questionnaire (A). Rather than the general
service website, product website and social networking website the questionnaire specifically
targets the loyalty perception for Google.com, Amazon.in and Facebook.com. The final
version of the questionnaire is listed in Appendix C.
Google.com (Google) is the world’s and India’s leading search engine and is providing
intangible service. Amazon.in (Amazon) is the one of the leading retail website in India. In
this study, Amazon is classified as product website that sells physical products. It also sells
intangible products (e.g. e-book) but excluding the few exceptions almost all items Amazon
sells are physical products. Facebook.com (Facebook) is the India’s number one social
networking website.
For validation purpose, these three successful websites were selected. The three
‘exemplars’ were chosen because:
1. The chances are more that any random Internet user is loyal to these websites as these
websites, as per Alexa (2014) – a web analytics company, are among the most visited
sites in their respective category.
2. In agreement with our proposed model, the practical strategies used by these websites are
discussed. The existing literature lacks such discussion.
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3. Explanation of strategies will serveas guideline to other upcoming websites. As
previously noted, more than 9 out of 10 business start-ups eventually shut down within
the first 120 days (Ducker, n.d.).
4. The discussion on strategies provides theoretical validation to the outcome of the
empirical analysis.
3.8 PRE-TEST AND PILOT-TEST
To validate the measurement instrument, a pre-test and a pilot-test were done. Pretest
group include 12 respondents who have at least three years of experiences and have a
preferable attitude toward a particular website in all three categories. They are asked to make
a judgment whether the constructs and measures are appropriate and in line with the purpose
of study. A detailed discussion was done about structure, wordings, length and format of the
instrument; several items were modified to reflect the questionnaire’s purpose more clearly.
The measurement items have been amended and rephrased without changing the intent of
items. Few were removed (either they were very specific to a particular website category or
were not appropriate for all categories) keeping in mind that same individual had to fill the
questionnaire items simultaneously for three websites. Thus item needs to be framed
accordingly and with clear understandability. For example, Item- ‘this website does not have
a tool that makes product comparisons easy’ is a very specific item related to product
website, thus removed.
The size of the pilot group may lie between from 25 to 100 (Cooper and Schindler,
2008). The pilot test involved 50 students, who prefer a particular product website, service
website and social networking website having online experience of more than a year. The
pilot study was conductedat the Jaypee University of Engineering and Technology, India.
Statistical Package for Social Sciences (SPSS) was used to measure the validity and
reliability. Cronbach’s alpha for all the items were greater than 0.70 as suggested by Nunally
(1978). The items loaded on the appropriatefactors for confirmatory factor analysis with
loadings greater than 0.50 as recommended by Hair et al. (2010). Thus, instrument confirmed
content validity and reliability. The result of thepilot test is listed in Appendix D.
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3.9 SAMPLING
This section explains the sampling size, sampling frame and sampling technique
adopted in this study. Saunders, Lewis and Thornhill (2009) defined four steps in sampling
process:
1. Ascertain a suitable sampling frame
2. Decide on a suitable sample size
3. Select the most appropriate sampling technique
4. Check the sample is representative of population
3.9.1 Sampling frame
A sampling frame is a comprehensive list of all the cases in the population (Cooper and
Schindler, 2008). The main purpose of this study is to analyze the same user loyalty
behaviour for three different kinds of websites - product website, service website and social
networking website. To achieve the objective, questionnaire orients to those online users who
have online experience and have a favourable attitude to at least one website in each
category. Approximately there are 290 million Internet users in India (Internet Live Stats,
2014). In the ideal case, the population consists of all these users; however, it is not feasible
and too expensive to gather a complete list of all Internet users in India. Moreover, it would
be impractical to identify our potential respondents out of the total number of Internet users.
It is not necessary that all users prefer at least one website in each category. The population to
be able to relate to this study, the participants included were the Internet users who visit their
preferred service website and social networking website at least five times a month and their
preferred product website at least twice a month.
3.9.2 Sample size
Many views exist regarding the sample size for research. Tabachnick and Fidell (2007)
suggests minimum five cases per item, Habing (2003) recommends at least 50 and five times
of variables and according to Field (2000) at least 10-15 subjects per variable. Hair et al.
(2010) suggested that sample size larger than 100 is adequate for factor analysis. Structural
equation modelling necessitates a large sample size because the model fit assumptions are
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based on a large sample size and according to Kelloway (1998) structural equation modelling
is appropriate with a minimum of 200 observations.
Following the above recommendations we aimed to achieve a sample size of 10-15
times per variable. In the study, there were 33 variables; a sample size in the range of 330-
495 would suffice. As such, we continued to invite respondents to take part in the research
until our sample size was reached.
3.9.3 Sampling technique
As discussed previously, the objective of this study is to analyze the same user
behaviour loyalty behaviour for different category of website and to determine the relative
importance of e-loyalty antecedents. Thus, our questionnaire orients to those online users
who have online experience of three classes of websites and necessarily the respondent
should prefer at least one website in the three categories. It is almost impractical to identify
our potential respondents out of the total number of Internet users.
In such cases, the target population is elusive, and other sampling methods (non-
probability sampling methods) must be employed (Lesley 2012). Saunders, Lewis and
Thornhill (2009) identified that in business research, the case may be that study does not have
appropriate sample frame to answer research question or do not have a sample frame at all,
alternatively, limited resources or the inability to specify a sampling frame may dictate the
use of one or a number of non-probability sampling techniques. Bhattacharjee (2012) also
explained that sometimes non-probability sampling is the only way to reach hard-to-reach
populations or when no sampling frame is available. It is reasonable to use non-probability
sampling technique for our study.
According to Statista (2013), a statistics portal contains statistics from more than
18,000 sources, distribution of Internet users in India by age group are: 15-24 (34%), 25-34
(38%), 35-44 (16%), 45-54 (6%) and 55+ (3%). Thus to identify our potential respondents
and to cater these different age groups; snowball sampling method was used in this study.
Although the generalizability is limited in non-probability sampling methods, but an
attempt has been made to ensure consistency and to improve the validity of results by
examining the proposed model in two different scenarios with the help of two questionnaires.
For validation purpose, the different segment was targeted, and convenience sampling
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method was used since the purpose is to cross-examine the results and to ensure
dependability in results. All the responses were obtained from students. Students were
selectedbecause:
1. India’s young and urbanizing consumer base offer growth potential for Internet usage and
will represent the future Internet usage patterns in the population at large (McKinsey and
Company, 2011).
2. Students can represent the online consumer population. Adopting students as a survey
sample is typically considered applicable to online consumers (Chang and Chen 2008,
Njite and Parsa 2005) since online consumers are generally younger and more highly
educated than conventional users, making student samples closer to the typical online
consumer population (McKnight and Chervany, 2002).
3.10 NON-RESPONSE BIAS
Non-response is the failure to collect information from sampled respondents (Leeuw,
Hox and Dillman, 2008). In case, a majority of respondents fails to respond or not interested
in survey lead to non-response bias if there are a legitimate concern and non-response due to
a systematic reason (Bhattacherjee, 2012). Fowler (2002) outlined three important
recommendations to reduce to non-response bias: (1) organization of the questionnaire should
be clear, (2) questions should be easy to read and nicely spaced, (3) questionnaire should be
respondent-friendly.
The three measures were followed during instrument development and validation.
Measurement items were adapted from existing studies that have already validated question
items. Also, pre-test and pilot-test was performed to make the questionnaire items user-
friendly and to ensure validity.
3.11 DATA COLLECTION
The data was collected separately two times. Data was obtainedfrom the user in each
categoryfor his/her preferred website through questionnaire (A). Questionnaire (B) was used
for data collection from a user for a particularwebsite (Google, Amazon and Facebook) in
each category.
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3.11.1 Questionnaire (A)
Data collection was mainly carried out by sending emails, direct communication with
some students, and instant communication with peers, friends and relatives who further
delivered the questionnaire to their peers and friends. Although snowball sampling does not
lead to representativeness but at times it is the best method available (Hsu et al. 2012) and
studies have applied this method in their research (e.g. Tong 2009; Lin and Sun 2009; Hsu et
al. 2012).
A total of 506 responses lied in our inclusion criteria out of 518, i.e. at least five visits
per month for service website and social networking website and two visits per month to the
product website. The responses of 13 respondents were eliminated as eight of them were
partially filled and five of them have given the same rating for all the items. Finally, 493
valid questionnaires were retained for analysis. All the respondents are from India. Sample
demography is provided in table3.2.
Table 3.2: Sample demography for questionnaire (A)
Measure Item Frequency %
Gender Male 386 78.30
Female 107 21.70
Age Under 18 15 3.04
18 to 30 305 61.86
30 to 40 166 33.67
40 to 50 5 1.01
> 50 2 0.40
Education Undergraduate 142 28.80
Graduate 159 32.25
Postgraduate 192 38.94
Occupation Student 121 24.54
Office Worker 337 68.35
Self Employed 27 5.47
Home Makers 08 1.62
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Respondents were from all age groups, but majority lie in 18-30 age-group. Males
comprised 78%, and female were 22% in which 68% were office workers. 62% of the
respondents fall in the age group 18 to 30.
Juxt (2011), a market research company, conducted a survey among 2,01,839 Indian
Internet users and stated that almost 2/3rd(66.6%) users were employed. The 25-35 years
segment is the largest online age group. Male users represented 73% while female user
consisted 27% of the Internet-using population. ComScore (2014) – a web navigation
analytics company, also confirmed the demography presented by Statista in 2013. The
Internet using Indian population in different age-groups, percentage wise are: 15-24 (36%),
25-34 (39%), 35-44 (16%), 45-54 (6%) and 55+ (3%). The demography of respondents in
this study is in line with the demography presented by Juxt (2011), Statista (2013) and
ComScore (2014). Figure 3.1, figure 3.2, figure 3.3 and figure 3.4 depict the gender-wise,
education-wise, age-group and occupation-wise information of the respondents, respectively.
Figure 3.1: Respondents gender
Figure 3.3: Respondents age-group
Figure 3.2: Respondents Education
Figure 3.4: Respondents occupation
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3.11.2 Questionnaire (B)
Two universities were approached, and students were asked to participate in the study.
Thus, all respondents are students from two different universities (ITM University and Jiwaji
University) of India. The questionnaire was delivered to them in person and through e-mail.
Total 375 responses has been received which lied in our inclusion criteria out of 385. Out of
375 questionnaires, 23 were invalid. Thus, 352 usable responses were obtained. Sample
demography is provided in table 3.5. The students less than 18 years of age were 23 % while
above 18 years consisted 77% of the total respondents. Figure 3.5 and figure 3.6 depict the
age-wise and gender-wise information graphically.
75% of the India’s online populations are under the age of 35 and individuals less than
15 years of age insignificantly represent India’s online population (Statista, 2013; ComScore,
2014). The students in our sample were in the range of 16-25 age-group. Table 3.3 provides
the sample demography for questionnaire (B).
Table 3.3: Sample demography for questionnaire (B)
Measure Item Frequency %
Gender Male 237 67.33
Female 115 32.67
Age Between 16 to 25
Occupation Student 352 100
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3.12 RESEARCH ETHICS
Widely accepted tenets of ethical behaviour in research are voluntary participation,
harmlessness, anonymity and confidentially, relevance of content, avoiding over-intrusive
question, fair analysis and reporting (Cohen, Marion and Morrison 2007; Bhattacherjee,
2012).
All responses obtained were well consented as the participation in the survey was
voluntary. However, no monetary and non-monetary benefits are provided to the participants.
The survey was self-administeredhence the participants are free to withdraw their
participation at any time. Respondents were communicated regarding safety and anonymity
and their personal information. Internet users were asked about their online loyalty perception
for their preferred online product/service provider thus the questions were relevant to them.
The questions were adapted from existing literature thus there is no threat or sensitivity issue
that lead respondents to over-reporting or under-reporting. It also ensured the content validity
of questionnaire. To measure reliability and validity pre-test and pilot-test were performed.
The issue of methodological rigor is an ethical issue, and respondents have a right to
expect validity and reliability (Cohen, Marion and Morrison 2007). Thus, factor analysis and
more rigorous statistical tests were applied to obtained data to ensure validity, reliability and
authenticity. All the findings and analysis are reported fairly.
3.13 DATA ANALYSIS METHOD
Data analysis in this study followed two-step approach suggested by Anderson and
Gerbing (1998). The first step conducted the factor analysis, reliability analysis and examined
convergent and discriminant validity of the measurement model. The second step assessed
the path significances of the research hypotheses, model-fit and variance explained by the
structural model.
3.13.1 Factor analysis
Kaiser-Meyer-Olkin (KMO) statistics and Bartlett’s test of sphericity was applied to
data prior to confirmatory factor analysis, to assess whether the data fits well with factor
analysis. KMO measure of sampling adequacy varies from 0 to 1.0, and KMO should attain
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a value 0.70 or higher to proceed with factor analysis (Dziuban and Shirkey, 1974). The
Bartlett’s test compares the observed correlation matrix to the identity matrix (Field, 2000).A
significant result (Significance level < 0.05) indicates matrix is not an identity matrix, i.e. the
constructs do relate to one another enough to run a meaningful factor analysis.
Factor analysis can be either confirmatory or exploratory. Based on the objective of
data analysis, each of these approaches can be implemented. Fabrigar et al. (1999) defined, in
situations where a researcher has relatively little theoretical or empirical basis to make strong
assumptions about existing common factors or how many distinct measured variables these
common factors are likely to influence, exploratory factor analysis (EFA) is probably a more
sensible approach than confirmatory factor analysis (CFA).But, when there is sufficient
theoretical and experiential basis to specify the model or small subset of models, CFA is
likely to be a better approach.CFA is often used in dataanalysis to investigate the expected
causal relationships between the variables and used when strong theory underlies
measurement model before investigation of data (Williams, 1995). The proposed structural
model of e-loyalty in this study was developed based on strong literature support, and there
exists a sufficient base for specified model.
Lu, Chang and Yu (2012) examined the online shoppers’ perceptions of e-retailers’
ethics, cultural orientation, and loyalty and used convenience sampling; applied regression
analysis to explore the relationships between variables. However, they indicated the use of
regression analysis as a limitation, and in fact they recommended confirmatory factor
analysis to establish unidimensionality for each factor and then structural equation modelling
for path analysis for future studies.Valvi and Fragkos (2012, p. 366) done a critical analysis
of e-loyalty studies and observed that “a final methodological limitation concerns the lack of
reporting or performing confirmatory factor analysis in certain studies”, thus not assessing
the models measurement fit”.Mouakket and Al-hawari (2012) adopted convenience sampling
and used AMOS to test the model fitness by performing CFA using SEM. The present study
also implemented CFA approach which is a special case of structural equation modelling
analysis with maximum likelihood estimation. AMOS 20.0 and SPSS 20.0 software packages
were used for the assessment of the measurement model and structural model.
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3.13.2 Reliability
Reliability is the extent to which the measure of a construct is consistent or dependable
(Bhattacherjee, 2012). It predicts the internal consistency of a model. The internal
consistency of the proposed model was determined by measuring Cronbach’s alpha and
Composite Reliability (CR). Nunally (1978) recommended, the model to be internally
consistent Cronbach’s alpha should be greater than 0.7 and every construct’s composite score
should be above 0.7 (Fornell and Larcker, 1981). If all the values are in the recommended
range than measurement items for each construct are reliable and stable, ensures data internal
consistency.
3.13.3 Validity
Validity specifies the degree to which an instrument measures what it is supposed to
measure (Kothari, 2008). Face validity or content validity is the first step in ensuring that
measurement items and questions are suitable to measure the constructs that are aimed to
measure (Bryman and Bell, 2007). Cronbach (1971) explained, content validity ascertains
that construct items are representative and drawn from auniversal pool.
Construct validity comprises of convergent validity and discriminant validity. Straub
(1989) defined, convergent validity ensures that there are relatively high correlations between
the measures of the same construct while discriminant validity confirms the low correlations
between the measures of different constructs that are expected to differ.Measurement of
convergent validity used three criteria suggested by Bagozzi and Yi (1988):
1. Factor loadings of all items should exceed 0.50 (Hair et al., 2010).
2. Composite reliability should be above 0.70.
3. Average Variance Extracted (AVE) of every construct should exceed 0.5 (Fornell and
Larcker, 1981).
AVE is the average amount of variance in observed variables that a latent construct can
explain (Farrell, 2009). To achieve discriminant validity, the square root of AVE should
exceed the inter-construct correlations below and across them (Fornell and Larcker, 1981).It
should be greater than maximum shared variance (MSV) and average shared variance (ASV)
(Hair et al., 2010).
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The measurement items used in this study has strong face validity as all the construct
items have been taken (with some adaptations to present study) from previous studies.
Definitions of e-service quality, trust, perceived value, number of members, number of peers,
satisfaction exhibits strong content validity in the existing literature, thus ensures content
validity of the construct items for this study. Pre-test and pilot-test were also performed to
ensure validity of the instrument.
3.13.4 Model fit
To evaluate the model fits, chi-square with degree of freedom (CMIN/df), the goodness
of fit index (GFI), the adjusted goodness of fit index (AGFI), normal fit index (NFI),
comparative fit index (CFI) and root mean square error of approximation (RMSEA) were
calculated. CMIN gives the minimum value of discrepancy between the data and the model.
CMIN/df is Chi-square divided by degrees of freedom. The goodness of fit index (GFI)
attains a statistical value between zero and one, indicates how well the model fits the data
where one indicates perfect fit (Joreskog and Sorbom, 1989). The Adjusted Goodness of Fit
Index (AGFI) adjusts the bias occurring from model complexity (Schermelleh-Engel and
Moosbrugger, 2003). It adjusts the degrees of freedom in relation to the number of observed
variables. The Degrees of freedom is the amount by which the number of sample moments
exceeds the number of parameters to be estimated. The Number of distinct sample moments
referred to are variance and covariance and the sample moments are the sample variances.
Normal fit index (NFI) indicates where the default model lies between saturated model and
independence model. Comparative fit index (CFI) compares the performance of the model
with baseline model. Baseline model assumes zero correlations between all observed
variables. Root mean square error of approximation (RMSEA) shows a lack of fit of the
model to population data.
The acceptable value of CMIN/df is less than three as suggested by Hayduck (1987).
Scott (1991) recommended GFI value to be greater than 0.90 and AGFI should be greater
than 0.80. NFI is in the acceptable range if its value exceeds 0.90 (Bentler and Bonnet, 1980).
Bagozzi and Yi (1988) recommended CFI to be acceptable if greater than 0.90 and also
suggested the RMSEA value should be less than 0.08.
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3.13.5 Choice of statistical analysis for path models
Generally, three approaches are used for testing the structural equation models or path
models: (1) PLS-PA, (2) System of regression equations, (3) AMOS-LISREL type search
algorithms. According to Westland (2014), PLS-PA are primarily exploratory analysis tools
and not suitable for hypothesis testing. Further the author suggested that PLS path estimates
are biased and highly dispersed when computed from small samples and is a ‘limited
information approach’ in a sense that path analysis implies that each of the Ordinary Least
Square(OLS) estimators on individual pairwise paths will, in most practical circumstances,
replicate the results of PLS path analysis software.
First generation models such as regression, Logistic Regression (LOGIT), Analysis of
Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA) can analyze only
one level of linkage between independent and dependent variables at a time (Gefen, Straub
and Boudreau. 2000). However, regression estimators are scaled, as recommended by Tukey
(1954) versus the un-scaled path coefficients of PLS-PA and AMOS-LISREL approaches.
The methods like multiple regressions were suitable for assessing constructs and relations
between constructs. The first purpose of regression analysis is prediction while the intent of a
correlation is to evaluate the relationship between the dependent and independent variables
(Tabachnick and Fidell, 2007). Contrary to first generation tools like regression, SEM
enables researchers to answer a set of interrelated research questions in a single, systematic
and comprehensive analysis by modelling the relationships among multiple independent and
dependent construct simultaneously (Gerbing and Anderson, 1988; Gefen, Straub and
Boudreau, 2000).
SEM using AMOS was chosen over PLS-PA and regression equations due to the
complex relationship between dependent, independent and mediating variables in proposed
model of present research. SEM permits complicated variable relationships to express
through hierarchical or non-hierarchical, recursive or non-recursive structural equations and
presents a complete picture of the entire model (Hanushek and Jackson 1977; Jan Recker,
2013). Westland (2012) suggested research is better served by a ‘full information method’
such as covariance approaches (e.g., LISREL, AMOS) or a system of equations approach.
Depending on the nature of the complex relationship between dependent, independent and
mediating variables, structural equation modelling appeared to be the most apposite methods
for addressing our research problems (Zweig and Webster, 2003).
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3.14 CONCLUSIONS
This chapter developed the conceptual framework and research methodology. Research
methodology described the stages in the research process. Most of the studies in social
sciences derived their foundation from positivist’s philosophy followed with a deductive
approach. Therefore, the current research fits well with positive philosophy with deductive
approach. For validation of conceptual framework, it is established that a quantitative
research approach would be more suitable than a qualitative one. Following this, the study
identified itself with explanatory studies; there exists a clearly organized problem
thatrequired data collection, prior to the collection of the data. Thus, for each research
construct measurement scales have been selected based on previously tested scalesthat
exhibited high consistency and validity. To measure the constructs and to address the
research issues, there is a need to identify a proper strategy for data collection. Survey
seemed the most appropriate choice. The study requires large number of responses to
generate the valid results. Thus, self-administered questionnaire appeared suitable method so
that the participant can fill the questionnaire at their convenience. A pilot study was
conducted to assess the reliability and validity of the questionnaire. After that, practical
concerns like sampling strategy and sample size were discussed. Justification for use of non-
probability sampling techniques and sample size was provided. This chapter also dealt with
non-response bias and research ethics.
An explanation of theappropriateness of factor analysis in this study and various
methods for assessment of modelfit were given. Justification for choosing CFA – a structural
equation modelling technique over other methods was provided. Criteria to assess the validity
and reliability of the obtained data were discussed. Reasons for suitability of SEM using
AMOS to test structural model and path significanceswere cited.
Chapter five presents and discusses the outcomes of hypothesis testing and determines
the relationships between dependent and independent variables.
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